4  Walkability

This analysis considers local walkability by calculating travel times to the closest public playground. Playgrounds are a useful candidate to assess the potential of walking to a nearby amenity as they are distributed evenly through the city with an average catchment of 600m (Figure 4.1).

Figure 4.1: Locations of council playgrounds designed with a 600m catchment.

4.1 Distance and speed on hilly terrain

Distance-based planning is convenient as it’s easy to calculate and objective. However, it doesn’t translate well to people’s decision making which are typically time-centric as well as accounting for some or all of the 5Cs of walkability (Chapter 1). Hence, even simple distance to time conversions can be more informative to the layperson planning a walking trip to the park. Figure 4.2 shows a conversion from distance to time assuming a speed of 5km/h - a common conversion based on a fit adult e.g. Section 3.4 in NZTA pedestrian planning and design guide.

Figure 4.2: Walkability maps coloured as both distance (m) and time (minutes).

Tobler’s function indicates that slopes can increase travel times considerably (Section 3.2). Heatmaps like Figure 4.2 are useful for a holistic picture but lack connection to the average citizen’s day to day life. Instead, the complexity from the walking time distribution is simplified with a Bayesian model of average walking times and spread in walking times by suburb. The forest plot in Figure 4.3 summarising the Bayesian model can be summarised even further with a quadrant classification. We can get the suburbs that lie in the four quadrants with some simple data filters. Popular suburbs of the city are listed from best to worst (in terms of local walkability to playgrounds).

Forest plot

Figure 4.3: Forest plot showing average ($\mu$) and spread ($\sigma$) in walking time to the nearest playground.

Quadrant classification

	High $\mu_{norm}$	Low $\mu_{norm}$
Low $\sigma_{norm}$	Consistently good walkability	Consistent but poor walkability
High $\sigma_{norm}$	Poor walkability for most areas	Good walkability for some areas

Suburb summary

suburb	quadrant	characteristic
Te Aro	Low $\sigma$ and $\mu$	Consistently good walkability
Newtown	Low $\sigma$ and $\mu$	Consistently good walkability
Pipitea	Low $\sigma$; High $\mu$	Consistent but poor walkability
Hataitai	Low $\sigma$; High $\mu$	Consistent but poor walkability
Newlands	High $\sigma$; Low $\mu$	Good walkability for some areas
Tawa	High $\sigma$; Low $\mu$	Good walkability for some areas
Brooklyn	High $\sigma$; Low $\mu$	Good walkability for some areas
Khandallah	High $\sigma$ and $\mu$	Poor walkability for most areas
Karori	High $\sigma$ and $\mu$	Poor walkability for most areas